ArticleSensors (Basel, Switzerland)2020
Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Evaluation of a Prediction-Based Bedtime Intervention in Reducing Nocturnal Low Glucose in Adults With Type 1 Diabetes: The DailyDose Bedtime Smart Snack Crossover Study.Diabetes care · 2025Trial
- Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- Multimodal Physiological Monitoring Using Novel Wearable Sensors: A Pilot Study on Nocturnal Glucose Dynamics and Meal-Related Cardiovascular Responses.Bioengineering (Basel, Switzerland) · 2026Article
- Development and validation of an interpretable machine learning model for predicting in-hospital hypoglycemia in adults with type 1 diabetes mellitus: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.Open life sciences · 2026Article
- Temporal gradient analysis of blood glucose responses to non-standard physical activity: a free-living study in type 1 diabetes.Frontiers in sports and active living · 2026Article
- Article
- Development of a Prediction Model for Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: The Epi-GLUREDIA Study.Nutrients · 2025Article
- Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes-An Integrative Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes.Journal of diabetes science and technology · 2025Article
- Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.BMC medical informatics and decision making · 2025Observational
- Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.Journal of diabetes science and technology · 2025Review
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.Scientific reports · 2024Article
- Bedtime Prediction of Nocturnal Hypoglycemia in Insulin-Treated Type 2 Diabetes Patients.Journal of diabetes science and technology · 2024Article
- Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.